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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Process Design and Techno-Economic Analysis of the Modular Staged Pressurized Oxy-Combustion (SPOC) Power Plant for Biomass

This work describes the process design and techno-economic analysis (TEA) of the modular SPOC power plant for biomass firing and coal-biomass co-firing. Two Rankine cycles were considered: a supercritical steam cycle (242 bar, 593°C, 593°C) with 550 MWe net output and a subcritical cycle (166 bar, 566°C, 566°C) with 200 MWe net output. For both cases, 95% carbon capture was modeled, and hybrid poplar biomass was chosen to generate carbon-negative power. In addition, the supercritical 500 MWe case included a 25% biomass co-firing (carbon neutral) case. For both cycles, a 100% Powder River Basin coal firing case was used for comparison purposes. In the SPOC process, oxygen is produced via a cryogenic air separation unit (ASU) and the heat generated from the compression of air is integrated into the steam cycle and utilized for boiler feed water pre-heating. Unique to the SPOC process, the boilers are pressurized and arranged in a series-parallel configuration, with minimized flue gas recirculation. The flue gas is cooled and scrubbed in the direct-contact cooler (DCC) column, and the moisture in the flue gas is condensed, leaving the bottom of the DCC at a sufficiently high temperature such that it can be used for boiler feed water pre-heating, improving plant thermal efficiency. Following drying and purification, CO2 in the flue gas is at the purity required for storage or utilization. The performance data were obtained from process modelling via Aspen Plus®. The stream data from Aspen Plus® were used as an input for the AACE Class 5 cost study. Ultimately, the capital costs, Levelized Cost of Electricity (LCOE), and cost of CO2 captured and avoided were obtained. The HHV efficiency of the carbon negative 550 MWe supercritical SPOC case (34.8%) was clearly above those reported by NETL for the BECCS baseline cases of supercritical pulverized coal with capture (B12B, 31.5%) and the 49% biomass co-firing case with capture (PA3, 29.2%). The HHV efficiency of the carbon-negative subcritical plant is also higher than the subcritical baseline PC plant with capture (case B11B.95) presented by NETL (32% vs 29.7%). The LCOE for the SPOC 100% biomass case was similar to the LCOE for the BECCS 49% biomass with carbon capture case ($147/MWh), and the SPOC carbon neutral case LCOE was lower ($110/MWh) than the cost for the NETL baseline SC coal firing case with 90% carbon capture ($114/MWh).

Magalhaes, Duarte↗
NASA NTRS1971

Decoder for delay-modulation coded data.

A decoding technique is described for the conversion of delay-modulated digital data to nonreturn to zero (NRZ) data. A potential time-phase ambiguity in reception and decoding of delay-modulated data is resolved in real time, through monitoring the data stream for a unique waveform inherent in delay-modulated data. Statistical backup is provided.

Lewin, J.↗
NASA NTRS1990

The impact of NRZ data asymmetry on the performance of a space telemetry system

The telemetry data asymmetry due to rising and falling voltage transitions can cause undesired spectral components at the output of a spacecraft transmitter. The performance of a space telemetry system can potentially degrade because of these undesired components. Here, an expression is derived for the power spectral density of an asymmetric nonreturn-to-zero (NRZ) data stream. This formula is then used to investigate how the data bandwidth varies with data asymmetry. At the receiver end, the threshold levels of undesired spectral components that fall into the carrier-tracking-loop bandwidth are determined by examining the derived spectral density. Further, based on this formula, a simple technique is developed for the computation of bit signal-to-noise ratio (SNR) degradation due to data asymmetry. The telemetry bit SNR degradations derived using this technique are compared with results obtained previously and with measurements by the Electronic Systems Test Laboratory (ESTL) at the Lyndon B. Johnson Space Center.

Nguyen, T. M.↗
NASA NTRS2006

Instrument Remote Control Application Framework

The Instrument Remote Control (IRC) architecture is a flexible, platform-independent application framework that is well suited for the control and monitoring of remote devices and sensors. IRC enables significant savings in development costs by utilizing extensible Markup Language (XML) descriptions to configure the framework for a specific application. The Instrument Markup Language (IML) is used to describe the commands used by an instrument, the data streams produced, the rules for formatting commands and parsing the data, and the method of communication. Often no custom code is needed to communicate with a new instrument or device. An IRC instance can advertise and publish a description about a device or subscribe to another device's description on a network. This simple capability of dynamically publishing and subscribing to interfaces enables a very flexible, self-adapting architecture for monitoring and control of complex instruments in diverse environments.

Ames, Troy↗
NASA NTRS2008

On Convergence of Development Costs and Cost Models for Complex Spaceflight Instrument Electronics

Development costs of a few recent spaceflight instrument electrical and electronics subsystems have diverged from respective heritage cost model predictions. The cost models used are Grass Roots, Price-H and Parametric Model. These cost models originated in the military and industry around 1970 and were successfully adopted and patched by NASA on a mission-by-mission basis for years. However, the complexity of new instruments recently changed rapidly by orders of magnitude. This is most obvious in the complexity of representative spaceflight instrument electronics' data system. It is now required to perform intermediate processing of digitized data apart from conventional processing of science phenomenon signals from multiple detectors. This involves on-board instrument formatting of computational operands from row data for example, images), multi-million operations per second on large volumes of data in reconfigurable hardware (in addition to processing on a general purpose imbedded or standalone instrument flight computer), as well as making decisions for on-board system adaptation and resource reconfiguration. The instrument data system is now tasked to perform more functions, such as forming packets and instrument-level data compression of more than one data stream, which are traditionally performed by the spacecraft command and data handling system. It is furthermore required that the electronics box for new complex instruments is developed for one-digit watt power consumption, small size and that it is light-weight, and delivers super-computing capabilities. The conflict between the actual development cost of newer complex instruments and its electronics components' heritage cost model predictions seems to be irreconcilable. This conflict and an approach to its resolution are addressed in this paper by determining the complexity parameters, complexity index, and their use in enhanced cost model.

Kizhner, Semion↗
NASA NTRS2017

The Kepler Data Processing Handbook: A Field Guide to Prospecting for Habitable Worlds

The Kepler telescope hurtled into orbit in March 2009, initiating NASA's first mission to discover Earth-size planets orbiting Sun-like stars. Kepler simultaneously collected data for approximately 165,000 target stars at a time over its four-year mission, identifying over 4700 planet candidates, over 2300 confirmed or validated planets, and over 2100 eclipsing binaries. While Kepler was designed to discover exoplanets, the long-term, ultrahigh photometric precision measurements it achieved made it a premier observational facility for stellar astrophysics, especially in the field of asteroseismology, and for variable stars, such as RR Lyrae. The Kepler Science Operations Center (SOC) was developed at NASA Ames Research Center to process the data acquired by Kepler from pixel-level calibrations all the way to identifying transiting planet signatures and subjecting them to a suite of diagnostic tests to establish or break confidence in their planetary nature. Detecting small, rocky planets transiting Sun-like stars presents a variety of daunting challenges, including achieving an unprecedented photometric precision of ~20 ppm on 6.5-hour timescales, and supporting the science operations, management, processing, and repeated reprocessing of the accumulating data stream. A newly revised and expanded version of the Kepler Data Processing Handbook (KDPH) has been released to support the legacy archival products. The KDPH details the theory, design and performance of the algorithms supporting each data processing step. This paper presents an overview of the KDPH and features illustrations of several key algorithms in the Kepler Science Data Processing Pipeline. Kepler was selected as the 10th mission of the Discovery Program. Funding for this mission is provided by NASA, Science Mission Directorate.

high performance computing↗
NASA NTRS2019

Thirty-Six Combined Years of MODIS Geolocation Trending

Two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been in operations for more than 19 and 17 years (thus 36 combined years) as part of NASA's Earth Observing System (EOS) on the Terra platform that was launched in December 1999 and on the Aqua platform that was launched in May 2002, respectively. Accurate geolocation is a critical element needed for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved MODIS geolocation accuracy in Collection-5 (C5), C6 and C6.1 re-processing and forward-processing data streams. We improved geolocation accuracy in the re-processed data and corrected for geolocation biases found in forward-processed data, including those caused by operations such as the stop-go-stop status of the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) instrument on the Aqua platform. We discuss scan-toscan underlaps near nadir over the equator regions that was discovered in checking the non-underlapping requirement in the Visible Infrared Imaging Radiometer Suite (VIIRS) based on trending parameters from the actual Suomi National Polar-orbiting Partnership (S-NPP) satellite orbit. The underlaps are closely tied to instrument effective focal length that is measured from on-orbit data using a technique we recently developed. We also discuss potential improvements for the upcoming C7 re-processing.

ground control point↗
NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗
DOE OSTI2024

Characterization of throughput on the AXI DMA bus for burst data transfer over Ethernet

cThe Xilinx AXI Direct Memory Access (AXI DMA) module is an efficient solution for medium-speed data transfer in Xilinx SoC FPGAs, supporting data rates greater than 1000 Gbps even in very suboptimal operating modes. It facilitates direct transfer of AXI stream data into processor memory without constant software intervention, which reduces overhead and ensures consistent data logging. By utilizing the FPGA's available memory, large circular buffers (1-5 GiB) are used to buffer data and accommodate network limitations, enabling high-rate data bursts. In this study, we measured the performance of AXI DMA under conditions simulating its lowest practical data transfer speeds. The Arbitrary Length Data Sender was used to transmit AXI stream packets at 32-bit width and 100 MHz frequency, a narrow width and slow speed. Results show that the AXI DMA can transfer up to 3192.76 Mbps with large packet sizes but experiences reduced performance for smaller packets, as low as 2.6 Mbps for 4-byte packets. For Ethernet-limited applications, packet sizes between 8,000 and 16,000 bytes provided optimal transfer speeds of 874 to 1600 Mbps. These findings suggest that the AXI DMA is not the limiting factor in systems where packet sizes exceed 8,000 bytes.

43 PARTICLE ACCELERATORS↗
NASA NTRS1992

On the use of stochastic process-based methods for the analysis of hyperspectral data

Further development in remote sensing technology requires refinement of information system design aspects, i.e., the ability to specify precisely the data to collect and the means to extract increasing amounts of information from the increasingly rich and complex data stream created. One of the principal directions of advance is that data from much larger numbers of spectral bands can be collected, but with significantly increased signal-to-noise ratio. The theory of stochastic or random processes may be applied to the modeling of second-order variations. A multispectral data set with a large number of spectral bands is analyzed using standard pattern recognition techniques. The data were classified using first a single spectral feature, then two, and continuing on with greater and greater numbers of features. Three different classification schemes are used: a standard maximum likelihood Gaussian scheme; the same approach with the mean values of all classes adjusted to be the same; and the use of a minimum distance to means scheme such that mean differences are used.

Landgrebe, David A.↗
NASA NTRS1992

CCD data acquisition systems at Lick and Keck Observatories

This paper will describe and compare two distinct but related CCD data acquisition systems (DAS) currently under development at Lick and Keck Observatories. Although these two systems have a number of major architectural differences, they share a considerable amount of common hardware and software. Both of these new systems build on a large body of proven software that is the foundation of the existing CCD DAS currently in use at Lick Observatory. Both will provide support for reading up to four on-chip amplifiers per CCD and/or reading out mosaics of CCD chips. In addition, they will provide the capability for interactive, real-time adjustment of CCD waveforms for engineering purposes. Each of these two systems is composed of three major subsystems: (1) an instrument computer and its software; (2) a data capture computer and its software; and (3) a CCD/dewar controller and its software. The instrument computer is a Unix workstation, and the functions it provides include user interfaces, the interactive real-time display of CCD images, and the recording of image and FITS header data to disk and/or tape. The data capture computer is responsible for the packaging and high-speed transfer of the CCD pixel data stream into a bulk RAM, and the subsequent transfer of this data to the instrument computer. The CCD/dewar controller generates the waveforms for clocking the CCD, digitizes the pixel data, and transmits it via high-speed link to the data capture computer. It is also responsible for monitoring and controlling the dewar temperature and cryogen levels. Given the number of different types of processors and high-speed data links employed in both systems, a major emphasis of this paper will be on the various forms of interprocessor communications utilized for data transfer and distributed process synchronization.

Kibrick, R. I.↗
NASA NTRS2009

On DESTINY Science Instrument Electrical and Electronics Subsystem Framework

Future space missions are going to require large focal planes with many sensing arrays and hundreds of millions of pixels all read out at high data rates'' . This will place unique demands on the electrical and electronics (EE) subsystem design and it will be critically important to have high technology readiness level (TRL) EE concepts ready to support such missions. One such omission is the Joint Dark Energy Mission (JDEM) charged with making precise measurements of the expansion rate of the universe to reveal vital clues about the nature of dark energy - a hypothetical form of energy that permeates all of space and tends to increase the rate of the expansion. One of three JDEM concept studies - the Dark Energy Space Telescope (DESTINY) was conducted in 2008 at the NASA's Goddard Space Flight Center (GSFC) in Greenbelt, Maryland. This paper presents the EE subsystem framework, which evolved from the DESTINY science instrument study. It describes the main challenges and implementation concepts related to the design of an EE subsystem featuring multiple focal planes populated with dozens of large arrays and millions of pixels. The focal planes are passively cooled to cryogenic temperatures (below 140 K). The sensor mosaic is controlled by a large number of Readout Integrated Circuits and Application Specific Integrated Circuits - the ROICs/ASICs in near proximity to their sensor focal planes. The ASICs, in turn, are serviced by a set of "warm" EE subsystem boxes performing Field Programmable Gate Array (FPGA) based digital signal processing (DSP) computations of complex algorithms, such as sampling-up-the-ramp algorithm (SUTR), over large volumes of fast data streams. The SUTR boxes are supported by the Instrument Control/Command and Data Handling box (ICDH Primary and Backup boxes) for lossless data compression, command and low volume telemetry handling, power conversion and for communications with the spacecraft. The paper outlines how the JDEM DESTINY concept instrument EE subsystem can be built now, a design; which is generally U.S. Government work not protected by U.S. copyright IEEEAC paper # 1429. Version 4. Updated October 19, 2009 applicable to a wide variety of missions using large focal planes with lar ge mosaics of sensors.

Kizhner, Semion↗
NASA NTRS2011

Restoration of Apollo Data by the NSSDC and the PDS Lunar Data Node

The Lunar Data Node (LDN), under the auspices of the Geosciences Node of the Planetary Data System (PDS), is restoring Apollo data archived at the National Space Science Data Center. The Apollo data were arch ived on older media (7 -track tapes. microfilm, microfiche) and in ob solete digital formats, which limits use of the data. The LDN is maki ng these data accessible by restoring them to standard formats and archiving them through PDS. The restoration involves reading the older m edia, collecting supporting data (metadata), deciphering and understa nding the data, and organizing into a data set. The data undergo a pe er review before archive at PDS. We will give an update on last year' s work. We have scanned notebooks from Otto Berg, P.1. for the Lunar Ejecta and Meteorites Experiment. These notebooks contain information on the data and calibration coefficients which we hope to be able to use to restore the raw data into a usable archive. We have scanned Ap ollo 14 and 15 Dust Detector data from microfilm and are in the proce ss of archiving thc scans with PDS. We are also restoring raw dust de tector data from magnetic tape supplied by Yosio Nakamura (UT Austin) . Seiichi Nagihara (Texas Tech Univ.) and others in cooperation with NSSDC are recovering ARCSAV tapes (tapes containing raw data streams from all the ALSEP instruments). We will be preparing these data for archive with PDS. We are also in the process of recovering and archivi ng data not previously archived, from the Apollo 16 Gamma Ray Spectro meter and the Apollo 17 Infrared Spectrometer.

Williams, David R.↗
DOE OSTI2024

System and method for wave prediction

A method and system for prediction of wave properties include collecting time series data streams from one or more wave measurement devices and processing the data using a wave-prediction algorithm to identify the frequency components of the data and compute wave parameters. The wave-field is propagated in space and time to predict wave height, speed, and velocity at a target location. A sliding window approach is used to continuously update the prediction in real-time.

Previsic, Mirko↗
DOE OSTI2024

System and method for wave prediction

A method and system for prediction of wave properties include collecting time-series data streams from one or more wave measurement devices and processing the data to identify data parameters to establish boundary conditions of a numerical model. The numerical model may be used to compute a predicted wave field of time-series data for a variety of wave properties at a target location.

Previsic, Mirko↗
NASA NTRS2017

Mining Twitter Data to Augment NASA GPM Validation

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

validatio↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Photometric Detection of Extra-Solar Planets

This NASA Origins Program grant supported the TEMPEST Texas McDonald Photometric Extrasolar Search for Transits) program at McDonald Observatory, which searches for transits of extrasolar planets across the disks of their parent stars. The basic approach is to use a wide-field ground-based telescope (in our case the McDonald Observatory 0.76m telescope and it s Prime Focus Corrector) to search for transits of short period (1-15 day orbits) of close-in hot-Jupiter planets in orbit around a large sample of field stars. The next task is to search these data streams for possible transit events. We collected our first set of test data for this program using the 0.76 m PFC in the summer of 1998. From those data, we developed the optimal observing procedures, including tailoring the stellar density, exposure times, and filters to best-suit the instrument and project. In the summer of 1999, we obtained the first partial season of data on a dedicated field in the constellation Cygnus. These data were used to develop and refine the reduction and analysis procedures to produce high-precision photometry and search for transits in the resulting light curves. The TeMPEST project subsequently obtained three full seasons of data on six different fields using the McDonald Observatory 0.76m PFC.

Hatzes, Artie P.↗